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Publication

Middle School Engagement with Mathematics Software and Later Interest and Self-Efficacy for STEM Careers

Research suggests that trajectories toward careers in science, technology, engineering, and mathematics (STEM) emerge early and are influenced by multiple factors.

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Journal of Science Education and Technology Special Issue - Stories from ITEST: Inspiring Young People to Pursue STEM Careers

The ITEST program has enabled creativity, experimentation, and cultural responsiveness in STEM education and workforce development and broadened participation in STEM initiatives to Native American communities, underresourced urban communities, girls, and populations underrepresented in STEM

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Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School

Research shows that middle school is an important juncture for a student where he or she starts to be conscious about academic achievement and thinks about college attendance.

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Affective States and State Tests: Investigating How Affect Throughout the School Year Predicts End of Year Learning Outcomes

In this paper, we investigate the correspondence between student affect in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year, on a high-stakes mathematics exam.

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Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System

Csikszentmihalyi’s Flow theory states that a balance between challenge and skill leads to high engagement, overwhelming challenge leads to anxiety or frustration, and insufficient challenge leads to boredom.

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Scientists Bring New Rigor to Education Research

This article in Scientific American (August 2014 issue), features the ITEST Research Study, Predicting STEM Career Choice from Computational Indicators of Student Engagement within Middle School Mathematics Classes (see page 72).  The article details efforts to

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This material is based upon work supported by the National Science Foundation under Grant Nos. DRL-1312022, 1614697 and 1949200. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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